Most training methods for detecting or classifying objects in video frames are trained by providing labeled example frames of a video. After the classier is trained, known test frames can be processed to determine a performance accuracy of the classifier.
Such methods perform well when training and testing is done in similar conditions, such as on the same scene. However, conditions often change because training and deployment can be in different scenes with widely varying illumination, camera position, apparent object sizes, and pose of the object. That is, often it can not be determined beforehand to what types of scene the classier will be applied.
It is object of the invention to adapt a general classifier to a particular scene, which is a particular scene that was unknown or not available when the classifier was trained.